Distinguished Platform Engineer

ServiceNow•Santa Clara, CA

13h•$279,100 - $488,400

About The Position

The Distinguished Engineer in HRSD sets technical direction for model-driven capabilities across the HR domain, including agentic and conversational experiences that interpret employee, manager, and HR agent intent; reason over profile, case, catalog, policy, and knowledge context; and act on users’ behalf. The role owns the architecture, standards, and evaluation infrastructure that enable multiple teams to ship these experiences safely, rather than owning a single feature set. This is not an ML research role and does not involve training foundation models. The work treats natural-language instructions, prompts, context, tool descriptions, and guardrails with the discipline applied to code, and establishes correctness through evaluation at scale because system behavior is probabilistic. The role defines AI architecture and autonomy boundaries for HR, maintains a versioned and upgrade-safe instruction and tool-description surface, and builds evaluation infrastructure and production safety practices. It also sets AI-assisted engineering standards, contributes hands-on to difficult integrations, migrations, prototypes, and incidents, and partners with product, engineering, and design to develop scalable AI solutions and robust technical designs.

Requirements

10+ years of software engineering experience, with a record of technical direction adopted beyond the immediate team.
Hands-on experience authoring agentic instructions and prompts, designing autonomous workflows, and building evaluations to verify them.
Production experience with LLM APIs, retrieval-grounded features, agent orchestration, tool and function calling, and structured-output enforcement, including at least one model or orchestration migration carried through production.
Experience with entitlement-aware data handling, including per-user access enforcement in retrieval and tool layers, scope separation between roles, and restricted records under audit.
Evaluation experience used by engineers beyond oneself, including dataset curation, judge calibration, CI gating, and drift detection.
Strong command of system design, APIs, data modeling, and testing across front-end, server-side, and relational data work, with a high-quality mindset.
Effective and accountable use of AI coding assistants and agents.
On-call and incident-command experience on customer-facing systems, including ownership of subsequent systemic fixes.
Experience with Java, JavaScript/TypeScript, and React, and demonstrated ability to quickly learn new tools and frameworks.
Bachelor's degree in computer science, software engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

HR domain depth in case management, employee journeys and lifecycle, or payroll, benefits, leave, or absence sufficient to set requirements.
Experience integrating with core HR, payroll, or benefits systems of record, including reconciliation and eventual-consistency patterns.
Contribution in a Forward deployment model to champion customer adoption.

Responsibilities

Set AI architecture for the HR domain, including agent decomposition and composition, reasoning placement, context assembly and limits, tool exposure, and delegation of autonomy.
Make multi-release decisions about model selection and migration, orchestration, build-versus-adopt choices, and cost, latency, and quality tradeoffs.
Define domain policy for which HR actions agents may take, which require human decisions, and which agents must not attempt; make human review structural for actions affecting pay, employment status, restricted records, or employee-relations matters.
Own shared instructions and tool descriptions as a versioned product contract, including upgrade-safe extension points, deprecation paths, and compatibility guarantees.
Build and maintain evaluation infrastructure, including golden datasets, multi-turn suites, judge calibration, CI gates, and drift detection; cover HR-specific failures such as access-boundary violations, cross-scope leakage, and jurisdictional or policy-variant correctness.
Establish production observability and safety practices for containment, hallucination, tool-selection errors, unsafe actions, and injection vectors while diagnosing issues without exposing restricted HR conversation content.
Define AI-assisted engineering standards, including specification standards, review expectations, and verification harnesses, and ensure teams follow them.
Contribute hands-on to difficult integrations, risky migrations, architectural prototypes, and incidents that need unblocking.
Collaborate with product, engineering, and design to co-create scalable AI solutions and translate business needs into robust technical designs.